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autonomous-agents

Design AI agents that break down goals, plan actions, use tools, and correct their own mistakes. The guidance focuses on reliable execution, clear operating boundaries, and managing conversation context as tasks grow longer.

OriginalShow the original description

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.

See how to install

Good for

  • Designing an agent's planning and execution loop
  • Breaking a complex goal into manageable tasks
  • Reviewing self-correction and reliability patterns
  • Managing conversation context during long tasks

Files included (2)

  • SKILL.md3.2 KB
  • references/detailed-guide.md26.8 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Autonomous Agents

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.

This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second.

2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth.

Detailed Guide

Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

Track context usage

class ContextManager: def init(self, max_tokens=100000): self.max_tokens = max_tokens self.messages = []

def add(self, message):
    self.messages.append(message)
    self.maybe_compact()

def maybe_compact(self):
    if self.token_count() > self.max_tokens * 0.8:
        self.compact()

def compact(self):
    # Always keep: system prompt
    system = self.messages[0]

    # Always keep: last N messages
    recent = self.messages[-10:]

    # Summarize: everything else
    middle = self.messages[1:-10]
    if middle:
        summary = summarize_messages(middle)
        self.messages = [system, summary] + recent

When to Use

  • User mentions or implies: autonomous agent
  • User mentions or implies: autogpt
  • User mentions or implies: babyagi
  • User mentions or implies: self-prompting
  • User mentions or implies: goal decomposition
  • User mentions or implies: react pattern
  • User mentions or implies: agent loop
  • User mentions or implies: self-correcting agent
  • User mentions or implies: reflection agent
  • User mentions or implies: langgraph
  • User mentions or implies: agentic ai
  • User mentions or implies: agent planning

Example

User request:

Use @autonomous-agents for this task: Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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